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Oracle Machine Learning

Machine learning uncovers hidden patterns and insights in enterprise data, generating new value for the business. Oracle Machine Learning accelerates the creation and deployment of machine learning models for data scientists using reduced data movement, AutoML technology, and simplified deployment.

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Build high performance machine learning models in Oracle Database

Oracle Machine Learning in Autonomous Database

Oracle Machine Learning Notebooks

Increase data scientist and developer productivity and reduce their learning curve with familiar open source-based Apache Zeppelin notebook technology. Notebooks support SQL, PL/SQL, Python, and markdown interpreters for Oracle Autonomous Database so users can work with their language of choice when developing models.

Oracle Machine Learning Services

Reduce time to deploy and manage native in-database models and ONNX-format classification and regression models outside Oracle Autonomous Database. Application developers have easy-to-integrate REST endpoints. Data scientists gain integrated model deployment from the Oracle Machine Learning AutoML User Interface.

Oracle Machine Learning for SQL

Simplify and accelerate the creation of machine learning models for both expert and non-expert data scientists alike, using familiar SQL and PL/SQL for data preparation, model building, evaluation, and deployment.

 

Oracle Machine Learning AutoML User Interface

A no-code user interface supporting AutoML on Autonomous Database to improve both data scientist productivity and non-expert user access to powerful in-database algorithms for classification and regression.

Oracle Machine Learning for Python

Data scientists and other Python users accelerate model building and execution by leveraging Oracle Autonomous Database’s built-in Python environment as a high-performance computing platform. Built-in AutoML provides automated algorithm and feature selection, as well as model tuning and selection. Together, these capabilities enhance user productivity, model accuracy, and performance.

Oracle Machine Learning in Exadata Cloud Service and Database Cloud Service

Oracle Machine Learning for SQL

Simplify and accelerate the creation of machine learning models for both expert and non-expert data scientists alike, using familiar SQL and PL/SQL for data preparation, model building, evaluation, and deployment.

Oracle Data Miner

Data scientists and data analysts can use this drag-and-drop user interface to quickly build analytical workflows. Rapid model development and refinement allows users to discover hidden patterns, relationships, and insights in their data.

Oracle Machine Learning for R

R users gain the performance and scalability of Oracle Database for data exploration, preparation, and machine learning using their language of choice. An integrated R interface provides easy deployment of user-defined R functions using SQL, making it easy to use CRAN libraries and packages.

Oracle Machine Learning in Oracle Exadata and Oracle Database

Oracle Machine Learning for SQL

Simplify and accelerate the creation of machine learning models for data scientists and citizen data scientists, using familiar SQL and PL/SQL for data preparation, model building, evaluation, and deployment.

Oracle Data Miner

Data scientists and data analysts can use this drag-and-drop user interface to quickly build analytical workflows. Rapid model development and refinement allows users to discover hidden patterns, relationships, and insights in their data.

Oracle Machine Learning for R

R users gain the performance and scalability of Oracle Database for data exploration, preparation, and machine learning using their language of choice. An integrated R interface provides easy deployment of user-defined R functions using SQL, making it easy to use CRAN libraries and packages.

Oracle Machine Learning in big data

Oracle Machine Learning for Spark

Data scientists can use an R API with scalable native and MLlib Spark-based algorithms on data from Hive, Impala, and HDFS for faster model building and data scoring on big data environments.

Oracle Machine Learning no code user interfaces

Oracle Machine Learning AutoML user interface

A no-code user interface supporting AutoML on Autonomous Database to improve both data scientist productivity and non-expert user access to powerful in-database algorithms for classification and regression.

Oracle Data Miner

Data scientists and data analysts can use this drag-and-drop user interface to quickly build analytical workflows. Rapid development and refinement allows users to discover hidden patterns, relationships, and insights in their data.

See how to build machine learning models faster with Python, R and SQL.

Just as some data warehouse clouds are trying to figure out how they play well with machine learning, Oracle has moved the goal posts by a lot,” said Marc Staimer, President of DS Consulting and Wikibon analyst. “Oracle’s Autonomous Data Warehouse now includes Auto-ML. ADW has included built-in machine learning since its inception. But now they’ve automated it so any ADW customer can use it without any expertise. This makes other offerings seem rudimentary and primitive by comparison.

Marc Staimer President of DS Consulting and Wikibon analyst

Oracle’s enhancements to Autonomous Data Warehouse are significant in three ways. First, it provides point-and-click user interfaces and machine learning automation, enabling non-professionals to generate actionable insights. Second, with this ease-of-use, even SMBs with small IT departments can get benefits from Oracle’s sophisticated cloud data warehouse. And, third, with Autonomous Data Warehouse, users can ingest, data from any source from departmental systems to enterprise data warehouses, data lakes, and even from other clouds—AWS, Azure, and Google and run diverse analytical workloads. All in all, Oracle is materially extending the reach of Autonomous Data Warehouse across users, organizations, and data access to multi-clouds. This transcends the barriers of what is possible today with AWS Redshift and Snowflake and any other cloud data warehouse on the planet.

Richard Winter CEO and Principal Architect

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Oracle Machine Learning customer successes

Customers around the world take advantage of Oracle’s in-database machine learning capabilities to solve complex and important data-driven problems.

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UK NHS saves £1.6 billion and provides better personalized care with Oracle Machine Learning.

Oracle Machine Learning benefits

  • In-database model building and data scoring

    Data scientists and developers build models and score data faster and at scale with no need to extract data to separate analytics engines. Oracle Exadata’s scale-out architecture and Smart Scan technology delivers fast results.

  • Security

    Data scientists and developers using Oracle Machine Learning are protected with Oracle Database built-in security, encryption, and role-based access to user data and models.

  • Rapid enterprise deployments

    Developers and the broader data science team achieve immediate machine learning model availability with easy deployment options using SQL and REST interfaces.

  • No data movement

    Data scientists and developers process data where it resides in Oracle Database, helping simplify model building and deployment, reducing application development time and improving data security.

  • High performance compute

    Data scientists avoid performance issues during data preparation, model building, and data scoring using the built-in parallelism and scalability of Oracle Database, with unique optimizations for Oracle Exadata.

February 23, 2021

Introducing Oracle Machine Learning for Python

Mark Hornick, Senior Director, Data Science and Machine Learning, Oracle

Data scientists and developers know the power of Python and Python's wide-spread adoption is a testament to its success. Now, Python users can extend this power when analyzing data in Oracle Autonomous Database. Oracle Machine Learning for Python (OML4Py) makes the open source Python scripting language and environment ready for the enterprise and big data.

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